1 Demystifying programming

Have you ever had only 5 minutes to hack into a high security intranet to stop nuclear missiles from hitting ground due to some cyber terrorist activity?

Well, me neither…

1.1 What is behind the illusion?

  • Speed is achieved by autocompleting with TAB and using only the keyboard when shortcuts are known.
  • After using the same code over and over you end up memorising it (hopefully).
  • Fail fast and often for cheap tasks. Set cheap tests for expensive tasks.

1.2 A dark souless void with a blinking cursor

GUI vs CLI

  • Rstudio as a GUI is also called Integrated Development enviroment (IDE). It has a CLI integrated under the Terminal tab.
  • R is running on the Console tab in Rstudio. Which is a CLI or terminal where R as program has been executed.

See the Getting Started for more information

2 Operators in R

2.1 A fancy calculator

Symbol Description
- Minus, can be unary or binary
+ Plus, can be unary or binary
* Multiplication, binary
/ Division, binary
^ Exponentiation, binary
%% Modulus, binary
%/% Integer divide, binary
%*% Matrix product, binary
%o% Outer product, binary
%in% Matching operator, binary (in model formulae: nesting)

Other mathematical functions are available such as exp(x), log(x), sqrt(x), abs(x), round(x, n), sum(x), max(x), etc.

2.2 A logical tool

Symbol Description
< Less than, binary
> Greater than, binary
! Negation
== Equal to, binary
>= Greater than or equal to, binary
<= Less than or equal to, binary
& And, binary, vectorized
&& And, binary, not vectorized

2.3 A programming language

Symbol Description
# Comment code, it won’t execute up to the end of the line
? Get help. e.g ?typeof
= Left assigment
<- Left assignment, binary
-> Right assignment, binary
$ List subset, binary
: Sequence or for specifying interaction in models

In R spaces or identation do not matter like in Python

3 Common data structures

Dimensions Homogeneous Heterogeneous
1 Vector / c(),: List / list()
2 Matrix / matrix() Data frame / data.frame()
>2 Array / array() Data frame / data.frame()

Factors are special class of vector that can only contain predefined values. They represent categorical data.

3.1 Indexing

Imagine that:

  • x is a data structure with multiple elements
  • i is an index or position in one dimension. It can be a number e.g. 1 (for the first position) or character if positions are named
  • j is another index for another dimension
  • a is a element name such a column name in a dataframe

NOTE: in R vectors start on 1 but in other languages such as Python it can be 0

# For vectors
x[i]
# Matrix or Data frame
x[i, j]
x[, j]
x[i, ]

# For lists and data frames
x[[i]]
x[[i, j]]
x$a

4 Common data types

Type in R / typeof() mode() Examples
logical logical TRUE, FALSE
integer numeric 1L, 10L, 569L
double numeric 4.20, 6.9, 1, 10
character character “data”, “doctors”, “cool”

An object x type…

  • can be tested with is._(x) where _ can be any of the available types. It returns a logical.
  • can be coerced to other type if possible using as._(x)

4.1 Missing values

Missing or unknown values in R are indicated by NA (Not Applicable). Unless removed, operating with “NAs” will yield an NA. Be careful to not mistake missing values with NULL since the last one is a different type with length 0 and without attributes.

5 Conditionals

5.1 if / else / else if

if ( statement1 ) {
    Do something
} else if ( statement2 ) {
    Do something else
} else if ( statement3 ) {
    Do something else
} else {
    Do something else
}
  • The statement used must return a logical (TRUE or FALSE). eg x > 5 or patient == "sick"
  • When there is a explicit condition defined one must use if or else if (for more than logical condition).
  • else conditional will execute when the defined condition is not met.
  • If we have a list of options then the function switch can be used.

6 Looping

# Risky due to unknown number of iterations
while ( statement1 ) {
    DO stuff until statement1 is FALSE
}

# Better use a for loop
for ( element in sequence ) {
    Do stuff based on element
}

There are extra controls for the loops:

  • break : exit the current loop that is being executed
  • next : starts the next iteration immediatly from the beginning of the current loop.

6.1 Special functions in R for looping

These are functions that apply a function (FUN) to each element of a different data structure (x):

  • apply(X, MARGIN, FUN, ...) where x is a matrix or dataframe, margin can be 1 to process rows and 2 for columns.
  • lapply(X, FUN, ...) where x can also be list and it returns a list.
  • sapply(X, FUN, ...) where x can also be a list but it returns a vector.

This are more efficient and fast ways to iterate in R. Always use if possible.

7 Functions

myfunction <- function ( argument1 = value1, argument2 = value2, ... ) {

    result = execute based on argument1 or argument2...
  
    return( result )
}
  • Arguments are passed as a list
  • return() is not necessary if the function just executes operations that are shown or elements stored outside the function

7.1 Object-oriented programming (OOP) vs Functional programming

Different stlyes of building the structure and elements of computer programs. Inspired from Wikipedia:

  • Object-oriented programming (OOP) is based on the concept of “objects” (often known as classes), which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods).
  • Functional programming (FP) treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data. The output value of a function depends only on its arguments, so calling a function with the same value for an argument always produces the same result.

In R:

  • Everything that exists is an object.
  • Everything that happens is a function call.

8 Packages or libraries

Due to the open-source nature of R, members of the community create collections of code, data, objects and/or functions available for everybody else. These are are provided as packages and stores in repositories (being CRAN the main one). Packages can be installed by using install.packages("package_of_interest") and loaded in the beginning of a session by executing library("package_of_interest")

10 Proposed exercise

Using all the elements described, let’s make a function that estimates the ten-year risk of fatal cardiovascular disease in Europe based on the SCORE project. Click here for the instructions in the paper, Appendix A

Conroy, R. M. et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24, 987–1003 (2003).

To simplify, just focus on one type of patients such as Non-CHD CVD and low risk.

You can compare your results based on the provided charts by the European Society of Cardiology

10.1 Tips

  • Copy and paste the syntax
  • Use the cheat sheet as a guide
  • Use if/else statements for the different cases
  • Create an example of a patient as a vector or a list
  • If you feel adventurous create multiple examples and run them by using a loop or even better with the apply() family


Thanks to the support of: